The article focuses on building and rigorously testing a server, deviating from an ongoing series on a financial data analyzer.
Tools like mypy, bandit, and Prospector are used to enforce type safety, security, and consistent code style.
The server improvement includes testability, model accuracy, code quality, and type consistency, with detailed code restructuring.
Tests cover WebSocket functionalities, integration-style tests, unit tests, and comprehensive analysis of various functions in the codebase.
The unit tests thoroughly cover functions like analyzing transactions, classifying transactions, recurring transactions detection, financial health calculation, and anomaly detection.
Integration-style tests demonstrate interaction with WebSocket handlers, error handling, and message processing ensuring robust server performance.
Static analysis tools like mypy for type checking, bandit for security, and black and isort for code formatting are employed to enhance code quality.
Comprehensive testing and static analysis result in 100% test coverage for the AI service, highlighting the commitment to reliability and performance.
The article concludes by inviting engagement and sharing, offering the author's expertise in software engineering and technical writing.